Large language models (LLMs) rely on the data they were trained on, resulting in static knowledge that prevents them from accessing or understanding events or information that emerged post-training. For example, LLMs trained before significant events such as the Russia-Ukraine war cannot provide relevant insights. To overcome this limitation, Retrieval-Augmented Generation (RAG) systems have been developed, which integrate dynamic information retrieval to supply current knowledge and enhance the capabilities of LLMs. Articles will introduce RAG models in detail as part of an informative series on new technologies in the software field.
Large language models (LLMs) are fundamentally based on static knowledge and lack the ability to access external information after their training is complete.
RAG (Retrieval-Augmented Generation) systems were developed to provide access to current, real-time information, addressing the limitations of standard LLMs.
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